The UCLA Drug Abuse Research Center (DARC) will establish a Field Research Collaborative Center to conduct analyses on the determinants of drug treatment outcomes using DATOS, DATOS-Adolescent, and CTOS within the framework of a treatment career perspective. Analyses can be augmented using data from DARC research studies and from other national, state, and local databases. DARC will participate in all collaborative activities as specified by the Steering Committee and provide input into ongoing research plans utilizing DATOS and other data. We propose a research plan to accomplish the following aims: To improve the understanding of drug treatment careers for adults and adolescents; to evaluate the relationship of treatment outcomes and co-morbid disorders; to assess the impact of program characteristics and processes on client outcomes during and after treatment; to examine the relationship between treatment system parameters and treatment outcomes; to understand the interactive relationship between treatment outcomes and the characteristics of clients, programs, and systems; and to foster access to and utilization of DATOS data and to disseminate research findings to a broad audience. These analyses will be accomplished using information from DATOS on the characteristics of clients in drug treatment programs, on the characteristics of these programs, on behavioral and attitudinal changes while in treatment, and on post-treatment status. In addition, we will draw upon data from DARC research which examines drug treatment outcomes within the context of a career perspective on drug use and its treatment. Complementary datasets can be used to test hypotheses derived from analyses of DATOS, to increase the number of sites and populations on which analyses are conducted, and to augment DATOS with more regionally specific and/or detailed datasets. Descriptive and multivariate analyses will be performed using a wide range of statistical techniques. Multi-level analyses will be conducted using advanced statistical methodologies including hierarchical linear modeling, event history analysis, Latent Curve Models and Latent Transition Models, structural equation modeling, and Markov modeling.